At COMPUTEX on Tuesday, NVIDIA announced NVIDIA JetPack 7.2 and NVIDIA NemoClaw support on NVIDIA Jetson .
JetPack 7.2 brings agentic AI skills, Yocto project support, NVIDIA CUDA 13 on NVIDIA Jetson Orin , a substantial performance gain on Jetson AGX Orin 32GB module and Multi-Instance GPU (MIG) support on NVIDIA Jetson Thor .
NVIDIA’s Asier Arrnaz shows how Build-a-Claw brings AI to the edge, a personalized, always-on assistant running right on NVIDIA Jetson.
The launch coincides with the GTC Taipei Build-a-Claw event , bringing the popular hands-on event from GTC San Jose to Taiwan, one of the world’s premier global technology hubs.
The release lands NemoClaw, NVIDIA’s agentic AI framework , on the production-grade Jetson stack — taking agentic AI from servers and workstations into the physical world, across robotics, inspection and industrial automation.
“Agentic AI is here, and Jetson’s programmability and high performance enable developers to instantly deploy physical AI agents in production at the edge,” said Deepu Talla, vice president of robotics and edge computing at NVIDIA. “With purpose-built skills for agentic development and workflows, developers can accelerate time to market, cut total cost of ownership and deploy at scale — all on a memory-optimized platform.”
Jetson is already a multi-generation platform — Orin , Thor and beyond — powering edge AI in robotics, autonomous systems, industrial inspection and medical devices. JetPack 7.2 builds on that foundation; NemoClaw extends it.
Three layers ship in this release. JetPack 7.2 at the base — operating system (OS), compute, deterministic performance. A new layer of agent skills in the middle, automating developer tasks. And NemoClaw at the top.
JetPack 7.2 brings major upgrades to the Jetson software foundation. Yocto-based OS support gives industrial customers a leaner, more customizable Linux foundation — important for memory-bound deployments. CUDA 13 on Jetson Orin brings the latest compute stack to existing devices. MIG plus real-time kernel on Jetson Thor lets developers reserve dedicated GPU resources for deterministic workloads, like robot perception systems that can’t pause for unrelated AI inference. Jetson AGX Orin 32GB also gets a performance boost to 241 TOPS of AI compute, up 20% above its original spec.
The middle layer — agent skills — accelerates the work of building a Jetson-based system itself. Jetson agent skills now include Linux customization, memory optimization, model benchmarking and similar developer tasks. These are now available as agent-deployable skills, developed from NVIDIA documentation and design guides. The result: a task that used to take weeks resolves in days.
At the top, NemoClaw deploys to Jetson with a single command. The pairing lands agentic AI on a production-grade robotics and vision AI stack, accelerating task automation for industrial systems. Developers can go further with NVIDIA Metropolis VSS blueprint skills , adding visual reasoning agents that watch, interpret and act on what they see.
The Jetson platform is already in deployment across fields such as robotics, industrial automation, drones, healthcare devices, agricultural machinery, humanoid systems and more.
Solomon uses NemoClaw to coordinate AI agents on a humanoid robot.
Key considerations
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Reference reading
- https://blogs.nvidia.com/blog/jetson-agentic-ai-physical-world/#primary
- https://blogs.nvidia.com/blog/author/chesu/
- https://blogs.nvidia.com/blog/jetson-agentic-ai-physical-world/#disqus_thread
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